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Fundamental statistical principles for the neurobiologist : a survival guide / Stephen W. Scheff, University of Kentucky Sanders-Brown Center on Aging, Lexington, KY, USA. -- Amsterdam : Academic Press is an imprint of Elsevier, c2016. – (59.591057/S316) |
Contents
Preface
About
the Author
1.
Elements of Experimentation
Reason
for Investigation
What to
Test
Levels
and Outcome Measures
Site
Preparation and Controls
Troublesome
Variables
What Do
You Do First When You Want to Run an Experiment
Types of
Experimental Design
Summary
References
2.
Experimental Design and Hypothesis
Hypothesis--Asking
the Right Research Question
Null
Hypothesis (Ho) and Alternative Hypothesis (HA)
What is
Probability Anyway?
Statistical
Significance
What is
a Significant Experiment?
One-Tailed
versus Two-Tailed Tests
Bias
Summary
References
3.
Statistic Essentials
Types of
Data
Nominal
Data
Ordinal
Data
Interval
Data
Ratio
Data
Discrete
and Continuous Data
Measures
of Central Tendency
Variance
Standard
Deviation
Standard
Error of the Mean
Confidence
Interval
Statistical
Myth Concerning Confidence Intervals
What is
Meant by "Effect Size"?
What is
a Z Score ?
Degrees
of Freedom
Why n-l?
Summary
References
4.
Graphing Data
How to
Graph Data
Box and
Whisker Plots
Scatter
Plots
Alternative
Graphing Procedures
Indicating
Significance on a Graph
Summary
5.
Correlation and Regression
Correlation
Pearson's
Product-Moment Correlation Coefficient
Spearman's
Rank Coefficient and Kendall's Tau
Regression
(Least Squares Method)
Summary
Reference
6.
One-Way Analysis of Variance
Analysis
of Variance
Student's
t-Test
Comparing
Three or More Independent Groups
Completely
Randomized One-Way ANOVA
Partitioned
Variance
Reporting
ANOVA Results
Homogeneity
of Variance
Multiple
Comparisons
Multiple
t-Tests
False
Discovery Rate
Common
Post Hoc Tests
How to
Choose Which MCP (Post Hoc) to Employ after an ANOVA
One-Way
Repeated Measures (Within-Subject) Analysis of Variance
Sphericity
Summary
References
7.
Two-Way Analysis of Variance
Concept
of Interaction
Difference
between One-Way and Two-Way Analysis of Variance
Interpreting
a Two-Way Analysis of Variance
Two-Way
Repeated Measure Analysis of Variance
Summary
8.
Nonparametric Statistics
Sign
Test
Wilcoxon
Matched Pairs Signed Rank Test (Wilcoxon Signed Rank Test)
Median
Test
Wilcoxon
Rank Sum Test (Mann-Whitney U Test)
Kolmogorov-Smimov
Two-Sample Test
Chi-Square
Fisher's
Exact Test
Kruskal-Wallis
One-Way Analysis of Variance
Friedman
One-Way Repeated Measure Analysis of Variance by Ranks
Spearman's
Rank Order Correlation
Kendall
Rank Order Correlation Coefficient
Nonparametric
and Distribution-Free Are Not Really the Same
Summary
References
9.
Outliers and Missing Data
Reasons
for Outliers
Removing
Outliers
Missing
Data
Summary
References
10.
Statistic Extras
Statistics
Speak
How to
Read Statistical Equations
Important
Statistical Symbols
Index
